Speaker 1: Bloomberg Audio Studios, podcasts, radio news.
Speaker 2: Good morning everyone. I'd hope that we'd start by talking
Speaker 2: about productivity and some of the data that you've just said, well,
Speaker 2: no one really knows. And the question with AI and
Speaker 2: the US economy is what has happened thus far? Right?
Speaker 2: And so I'll hit you with some of the official
Speaker 2: data that I've been tracking, and you can tell me
Speaker 2: whether the utility of it or not. Right, which is
Speaker 2: really in US productivity? I always go with the Bureau
Speaker 2: of Labor Statistics measure of output per hour x non
Speaker 2: farm business sector. Right, And if you look at the
Speaker 2: data over fifty years, that chart was really interesting, the
Speaker 2: side by side of electricity and AI over fifty years,
Speaker 2: the average quarterly reading is about one point nine percent
Speaker 2: annual rate on productivity. But something's happened in the last
Speaker 2: ten quarters where it's higher, Yes, you know, just under
Speaker 2: three percent two point seven percent. Do we really know
Speaker 2: what that is? And is it AI?
Speaker 3: We don't know.
Speaker 1: I mean, that's the part that makes it hard is
Speaker 1: in productivity numbers, especially when they're happening in what I
Speaker 1: would think of as real time, it's very challenging to
Speaker 1: assess or draw it back to exactly what the factors
Speaker 1: are that have shaped it. You know, in fact, people
Speaker 1: still don't agree on what happened in the nineties all
Speaker 1: the time if you look at research. So it's just
Speaker 1: something to keep in mind. So then what you do
Speaker 1: would any good economist or person, any industry person would do,
Speaker 1: is they'd say, well.
Speaker 3: What am I seeing? What am I seeing?
Speaker 1: And so right now, while we can't find it in
Speaker 1: the macro studies, it would do very sophisticated empirical econometrics
Speaker 1: and ask the questions how much of this is AI.
Speaker 1: We still can see that there's something going on there.
Speaker 1: The question is is it happening? The question is how
Speaker 1: long will it persist? And so clearly something's happening in
Speaker 1: the economy. But if you make a series to go
Speaker 1: back to your question about productivity, if you make a
Speaker 1: series of one time adjustments, so say you automate a
Speaker 1: production line or you use AI to help in loan
Speaker 1: application process, you save money once. You don't save money forever.
Speaker 1: I mean, you keep saving that money, but you don't
Speaker 1: get growth out of that. You don't get productivity growth.
Speaker 1: You get one time adjustments to the level of productivity of.
Speaker 3: Your employees or your process.
Speaker 1: So what we're looking for is a technology to give
Speaker 1: us consistently good changes in productivity so that all industries
Speaker 1: at scale get better, industries figure out new ways to
Speaker 1: generate revenue, new ways to do product design, new ideas
Speaker 1: to come and shape the economy. That's the thing that
Speaker 1: has a sustained productivity growth part. So it's undeniable productivity
Speaker 1: growth has gone up. What's not as clear is how
Speaker 1: long will that last.
Speaker 2: Broadly, people want to see and understand how AI impacts
Speaker 2: workforce and more recently maybe inflation. So if we go
Speaker 2: back to the nineties and what green Span saw in
Speaker 2: productivity gains contributing to economic growth, there was a consideration
Speaker 2: around both of those things. Absolutely, you said that it's
Speaker 2: not the playbook to go back to what happened nineties
Speaker 2: and apply today, But what do you see in those things?
Speaker 2: Is it possible that AI is driving productivity games resulting
Speaker 2: in economic growth, but without the inflation it is.
Speaker 1: Absolutely possible and something we have to interrogate. I mean,
Speaker 1: right now, as you know to well, inflation still above
Speaker 1: our target are two percent target, and price level has
Speaker 1: been high for much higher for a long time, and
Speaker 1: people are feeling stretched by the high inflation that they see.
Speaker 1: And now oftentimes people say, well, now AI is going
Speaker 1: to take hurt the labor market, and so now I'm
Speaker 1: in double doom, as people say. But I think ultimately
Speaker 1: the way you think many people think about AI is
Speaker 1: the investment part of any technology can actually boost demand
Speaker 1: for good services in people and can then raise the
Speaker 1: pressure on inflation. But then the productivity part comes and
Speaker 1: that that's a disinflationary part.
Speaker 3: You can see.
Speaker 1: This is all about the timing, and so what we
Speaker 1: end up investigating is not just the models but asking questions.
Speaker 1: Are the buildout of data centers raising prices for construction workers,
Speaker 1: are the buildout of data centers raising prices for metals
Speaker 1: and other things that go into them? The raw materials
Speaker 1: are the productivity gains. And then on the other side
Speaker 1: of that, are the productivity gains already affecting the cost
Speaker 1: structure of firms? Do they see that and even if
Speaker 1: a series of one off adjustments can actually change the
Speaker 1: cost structure, And if you look at profit margins when
Speaker 1: prices haven't been raising as rapidly as they once were,
Speaker 1: and firms are saying they don't have as much power
Speaker 1: to pass through, you would think that they're doing something
Speaker 1: to help margin protection. And so I think this is
Speaker 1: there's something going on here. Whether we wanted to link
Speaker 1: it all back to AI and then use that as
Speaker 1: a demonstrated proof that we're in a transformative state, I
Speaker 1: think that's a little bit too far, but certainly something's happening.
Speaker 1: And thinking about the productivity growth is exactly what you
Speaker 1: know we did back in the nineteen nineties. We saw
Speaker 1: evidence firms were being more productive. We were interrogating how
Speaker 1: long that would last. And interestingly, the nineteen nineties when
Speaker 1: I said it was the Roaring nineties that followed. It
Speaker 1: was good growth, but it was also a good labor market,
Speaker 1: a really strong labor market, and so those two things
Speaker 1: went together. Because ultimately we had this conversation in the
Speaker 1: roundtable and one of the participants made a great point,
Speaker 1: it's true economics.
Speaker 3: This is how economics works.
Speaker 1: Is if an employee using AI gets much more productive,
Speaker 1: you hire more of them.
Speaker 3: Right, not fewer of them.
Speaker 1: So you know, the economy grows faster, the product development
Speaker 1: goes goes faster, and demand gets stronger.
Speaker 2: I'm going to jump ahead to data center I'd been
Speaker 2: saving it, but it's highly relevant to San Jose the
Speaker 2: build out of data center. Very recently, the CEO of
Speaker 2: PG and E, Patty Poppy, Game on the program and
Speaker 2: made the argument that it's possible that the data center
Speaker 2: build out within PG and e's jurisdiction actually brings down
Speaker 2: wholesale electricity prices because the hyperscalas take on the capital
Speaker 2: burden and they are buyers and aggregate of electricity. But
Speaker 2: many people, you know, your constituents in the twelfth district
Speaker 2: will find it hard to see that argument playing out.
Speaker 1: Well, I think we have to separate what we're talking
Speaker 1: about into now, next, later.
Speaker 3: So let's think about now.
Speaker 1: Right now, we have more demand than we have supply
Speaker 1: for energy for electricity. Right if you talk to you,
Speaker 1: we regularly have CEO round tables with the big power
Speaker 1: companies across the twelfth district. You can look throughout the nation.
Speaker 1: Demand for power is higher than the supply of power,
Speaker 1: and there's a lot of reasons why supply is falling behind.
Speaker 1: One is demand's just gone up rapidly, but another part
Speaker 1: is that they've got an aging infrastructure. They have to
Speaker 1: get those things built out. It's a highly regulated industry,
Speaker 1: so the infrastructure doesn't just come on like a light switch.
Speaker 1: Then you have there are supply chain issues that made
Speaker 1: it hard to get the transformers and other things. So
Speaker 1: all of this just adds to the imbalance of demand
Speaker 1: versus supply. But the remedy for that isn't to take
Speaker 1: away demand, it's to increase supply.
Speaker 3: So when.
Speaker 1: They talk about any CEO of a power company says,
Speaker 1: we can solve this problem by adding more supply. Absolutely,
Speaker 1: but that's a next and later. And so what you said,
Speaker 1: my constituents, what consumers and businesses are saying is I'm
Speaker 1: worried my electricity prices are going to rise, and they've
Speaker 1: already been going up. And the CEOs of power companies
Speaker 1: are saying, but if we just keep building, that will
Speaker 1: go down, and both are true.
Speaker 2: Go down as far as it will be disinflation.
Speaker 1: And you know, it's hard to say energy could be
Speaker 1: disinflationary if we get to a point where supply is
Speaker 1: greater than demand. Right now, I'm just looking for a
Speaker 1: supply to equal demand, and that would be a big
Speaker 1: benefit to consumers because it would mean that we wouldn't
Speaker 1: keep seeing inflationary pressure coming out of the energy sector.
Speaker 2: The other thing I wanted to ask you through the
Speaker 2: lens of constituents of the twelfth districts is one reason
Speaker 2: you might focus on productivity is there is a direct
Speaker 2: read through to GDP growth and other data sets that
Speaker 2: you can look at. But the anxiety in the real
Speaker 2: world is, well, a job, an AI talk can make
Speaker 2: me more productive or it can displace me altogether. Where
Speaker 2: do you see that tension in the economy right now?
Speaker 3: So one of the.
Speaker 1: Things that is true is that the labor market has slowed,
Speaker 1: but it slowed for a whole variety of reasons. And
Speaker 1: much like when you said, well, productivity is risen, Mary,
Speaker 1: so shouldn't we isn't that AI? I think we always
Speaker 1: want to be a little humble about the correlations we
Speaker 1: see and ascribing.
Speaker 3: Causality to them.
Speaker 1: So I wanted to temper your enthusiasm for thinking all
Speaker 1: the productivity growth is AI might be, but it could
Speaker 1: just be general cost management in a slowing economy or
Speaker 1: a slowing you know, or to manage tariff costs, etc.
Speaker 3: So on the labor market. The labor market is slowing.
Speaker 1: It's slowing in industries that are directly telling us that
Speaker 1: they're using AI and it's slowing in industries that aren't.
Speaker 1: So it's one of the things that I just want
Speaker 1: to be cautious. So what I talk to, We talked
Speaker 1: to workers, We talk to you know, communities all the time.
Speaker 1: What's true is in technologies is a really interesting thing.
Speaker 1: No technology ever reduces net employment, not in the history
Speaker 1: of technologies, but it does change what that employment looks like.
Speaker 1: And so there's a period of replacement right now. It's
Speaker 1: replacement of tasks. So if your job has certain tasks
Speaker 1: in it, AI can do those for you. And the
Speaker 1: next part is augmentation, so every place augment and create.
Speaker 1: What's interesting about AI is that, unlike say electricity, when
Speaker 1: the candle lighters or the lamplighters or the candle makers
Speaker 1: got displaced before the US completely became electrified, you know
Speaker 1: that this is going more quickly if you go to
Speaker 1: a firm. I was on a panel at the Reagan
Speaker 1: National Library Economic Forum with Patrick Collison from Sprint. I'm Sprints, right, Gosh, Stripe, Sorry,
Speaker 1: he's going to kill me, Stripe, don't tell him, okay,
Speaker 1: but from Stripe, and and interestingly he said, I am
Speaker 1: hiring more coders that I'm laying off, but I am
Speaker 1: laying off coders whose technology skills didn't advance or they
Speaker 1: weren't the right workers.
Speaker 3: And you're seeing this right.
Speaker 1: You're seeing you know, businesses reskill them their cells to
Speaker 1: meet the AI moment, and that's going to cause worker anxiety.
Speaker 1: And right now, worker anxiety is high. People were a
Speaker 1: low firing, low hiring environment. That's already going to make
Speaker 1: people feel vulnerable. If you haven't found a job and
Speaker 1: you're newly minted out of college, you just think I
Speaker 1: was supposed to get a job before I graduated. Now
Speaker 1: I still don't have one. That's very worriesome to people.
Speaker 1: And then if you're thinking, well, I might lose my job,
Speaker 1: but I don't know how long it will take to
Speaker 1: get another one, then you're worried about that. So I
Speaker 1: think it's natural for the sentiment to feel nervous. But
Speaker 1: it's not the same things. AI is taking all the jobs,
Speaker 1: because what we're really seeing is AIS is replacing tasks,
Speaker 1: augmenting workers. When we talk to firms, most of those
Speaker 1: firms are saying I'm augmenting my workforce.
Speaker 3: If you're in big.
Speaker 1: Manufacturing firms, they don't have enough workers that do skilled labor,
Speaker 1: and so they're looking to augment their workforce, and then
Speaker 1: we're also seeing jobs created. It's interesting I gave this talk.
Speaker 1: I gave a talk on this in twenty twenty three,
Speaker 1: and I used prompt engineers as the jobs they were creating.
Speaker 1: But those jobs are now being replaced.
Speaker 3: But a.
Speaker 2: Or it's a change, it's.
Speaker 1: A warning, you could think of it that way. Or
Speaker 1: it's an indicator. So let's take the warning. The warning
Speaker 1: is you can't keep up. I would say, let's use
Speaker 1: it as an indicator. It's an indicator that technology is
Speaker 1: evolving really fast and workforces need to skill endurable skills,
Speaker 1: and durable skills.
Speaker 3: Are be AI ready?
Speaker 1: Be able to use AI to lift yourself in the
Speaker 1: educational space. You know, use the use the technologies that
Speaker 1: are out there to build your skills up, because you
Speaker 1: can learn a lot fast if you train yourself to
Speaker 1: look at AI.
Speaker 3: Say give me a lesson on how.
Speaker 1: To write I've been thinking about this, how to write
Speaker 1: a smart contract from end to end? What sort of
Speaker 1: software would I need? How would I do it? What
Speaker 1: would the code look like? How would I test the code?
Speaker 1: How would I know it's right? Before I execute on
Speaker 1: this smart contract, and so you can do these things
Speaker 1: in an evening and then it's just about being able
Speaker 1: to do that. So I think that's the message for workers,
Speaker 1: and I would have taught my young self this same thing.
Speaker 1: Is if you put off technology because you're afraid of it,
Speaker 1: then you won't be in the first place of trying
Speaker 1: to use the technology to further your own abilities.
Speaker 2: Can we extend that to the FED? Now bear with
Speaker 2: me on that one. Okay, all right, you talked about
Speaker 2: disaggregated data but also improved measurement, citing Greenspan in that sense.
Speaker 2: If AI is so good, can it process larger sets
Speaker 2: of data and make more accurate economic forecasts than traditional
Speaker 2: FED models?
Speaker 1: Can? We know we don't right now use AI in
Speaker 1: our monetary policy work, but we do use it in
Speaker 1: research as researchers. If you go to any academic institution,
Speaker 1: you're going to see researchers using AI to see what
Speaker 1: they can do better on coding and other things, but
Speaker 1: also data analytics.
Speaker 3: What do you see The place.
Speaker 1: We are there is AI doesn't give you answers to problems.
Speaker 1: It helps you get to the discovery perspective. So if
Speaker 1: I use AI as a researcher to look at a
Speaker 1: bunch of data. I still have to test my hypothesis.
Speaker 1: I have to go in with a hypothesis. What am
Speaker 1: I trying to answer? So that's the human person. And
Speaker 1: so that's why it's not particularly well tooled right now
Speaker 1: to replace our forecasters and our thinkers, our scholars who.
Speaker 2: Comproduce a more accurate neutral rates.
Speaker 1: For example, Well, you're still going to get estimates that
Speaker 1: are between eleven and negative three on the neutral rate
Speaker 1: of interest, So I'm not kidding. Models can the models
Speaker 1: that we have can produce an estimate from negative three
Speaker 1: to positive eleven, right, And so there.
Speaker 3: What does that tell you?
Speaker 1: The the neutral rate of interest is not a truth
Speaker 1: with a capital T.
Speaker 3: It's an estimate.
Speaker 1: It's a theoretical construct to help us understand how to
Speaker 1: benchmark policy. But you can't use it as a threshold
Speaker 1: that you can do surgical adjustments around. No one calibrates
Speaker 1: monetary policy surgically with a neutral rate of interest estimate
Speaker 1: for those reasons. So we are using AI though at
Speaker 1: the FED, and many people may be surprised about that.
Speaker 1: Would you like to learn about that?
Speaker 2: Yes? Please help?
Speaker 1: So I know many might think I work in an
Speaker 1: institution that waits for elect we're still getting electricity. You
Speaker 1: might think that, but no, we actually are not the
Speaker 1: earliest adopters because remember we're fiduciary stewarts of public funds
Speaker 1: but also as fiduciary stewards of public trust, and so
Speaker 1: we really have to make sure that we're working in
Speaker 1: the most risk free and risk managed environment we possibly can't.
Speaker 1: But we have been at this since really in twenty
Speaker 1: twenty three. So the first thing that we did it
Speaker 1: as a system, and I'll really speak about the twelve
Speaker 1: at a reserve banks that are across the country. We
Speaker 1: worked as a system to say, well, we need to
Speaker 1: make sure our employees, our teams are ready to understand AI.
Speaker 3: So what do we need to do.
Speaker 1: We need to have lessons, work playing, you know, work gatherings,
Speaker 1: et cetera, get people familiar and get people focused in
Speaker 1: areas that we can practice with. So we built a
Speaker 1: practice environment that was completely ring fenced and not in production. Right,
Speaker 1: it's just a practice environment trying it out, and of
Speaker 1: course we got what most businesses got. The other businesses
Speaker 1: did exactly the same thing.
Speaker 3: And what do you get? You get the early adopters.
Speaker 1: But the good news about our early adopters as are
Speaker 1: often ambassadors. So then we're holding like tech cafes and
Speaker 1: things to help other people learn that.
Speaker 3: So that was then.
Speaker 1: So then in twenty twenty four and twenty five we
Speaker 1: really made a full court press push March Madness is
Speaker 1: coming up.
Speaker 3: You know.
Speaker 1: We really went hard at making sure that people had
Speaker 1: not just the if they're interested, do it, but that
Speaker 1: this is something that we really want.
Speaker 3: You to learn.
Speaker 2: This is the operations within the system.
Speaker 1: This is the operations. I should have said that before.
Speaker 1: I'm sorry. Let'll tell you that if you were at
Speaker 1: a reserve bank, and again little known facts. These are
Speaker 1: like facts that people don't know about the FED if
Speaker 1: you go to a reserve bank or any of our operations.
Speaker 1: Most of the people who work with us are operations people.
Speaker 1: We process cash, We do all the electronic payment system backbones,
Speaker 1: make sure they operate on time. If you're in the
Speaker 1: financial sector, you know FED wire or acch FED Now
Speaker 1: all of that is operated by.
Speaker 3: Our operations teams. We also support Vice Chair.
Speaker 1: Bowman in supervision of banks and all of those things.
Speaker 1: And then we have all our support people who help
Speaker 1: make sure that that occurs. All of that can be
Speaker 1: easily if you can do AI and you can use it,
Speaker 1: you can think of opportunities. So the next thing we did,
Speaker 1: get our workforce ready is number one. For next thing
Speaker 1: you do is and this is again like all the
Speaker 1: businesses we talk to, you see what your vendors are
Speaker 1: our offering, right if you're you have a technology vendor,
Speaker 1: an accounting vendor, an HR vendor, and then you just
Speaker 1: turn the service on. But if you turn the service
Speaker 1: on before your team is ready, you don't really get
Speaker 1: an ROI out of it. So again, fiduciary stewarts of
Speaker 1: public funds, we have to make sure so we're all
Speaker 1: instilled in this ring fenced proprietary environment because the public
Speaker 1: has to know that we're not introducing risk to this.
Speaker 3: And then of course the place we're.
Speaker 1: Working now is where many many people are working, which
Speaker 1: is if you have a lot of technology workers, then
Speaker 1: the coding assist is just so important and we're one
Speaker 1: of the things back to workforce. It doesn't create a
Speaker 1: massive change in who you have working for you. What
Speaker 1: it means is they can do their work faster, better
Speaker 1: and more effectively. And if you think of the three
Speaker 1: timples of the FED, we need to be efficient, effective.
Speaker 3: And resilient.
Speaker 1: So it also builds in that resilience for us because
Speaker 1: we have you know, quality assurance and unit testing. All
Speaker 1: the things that can slow you down if you're not
Speaker 1: right or interrupture your ability to serve. Those things can
Speaker 1: all be assisted with AI in a really positive way. So,
Speaker 1: again not monetary policy, but definitely like all other companies
Speaker 1: who are working on this space, making sure we're not
Speaker 1: behind and delivering good value for I mean, the shareholders
Speaker 1: of the FED are the American people, and we owe
Speaker 1: them the effort to make sure we're modernizing ourselves and
Speaker 1: keeping up with the things that can help us do
Speaker 1: our work faster, better, and more effective.
Speaker 2: I do have two questions relating to AI and monetary
Speaker 2: policy quickly, and I know we want to get some
Speaker 2: audience questions as well, some conscious there are students in
Speaker 2: the room who will go out into the workforce. I
Speaker 2: think that the main thing reflecting back on the nineties
Speaker 2: is that there are anticipated impacts yes from AI on
Speaker 2: the economy and PCEE is the preferred gauge inflation running
Speaker 2: higher beyond two percent. How do you manage that. You know,
Speaker 2: many would argue that those anticipated AI driven productivity gains
Speaker 2: would justify lower rates, but they are that anticipated, yeah, and.
Speaker 1: I think that, you know, really, it's important to recognize
Speaker 1: that monetary policy is a forward looking business, but it's
Speaker 1: also an evidence based business. And so there will be
Speaker 1: a point in time when we'll have enough confidence that
Speaker 1: the anticipated effects are materializing. And where would you look
Speaker 1: for that? You look for that in what's happening with
Speaker 1: price pressures? Not just aggregate inflation, but if you disaggregate
Speaker 1: it and you ask yourself a question, are the AI
Speaker 1: using sectors just doing less pass through of input costs
Speaker 1: into prices? Well, maybe that tells you something. So that's
Speaker 1: where the research really becomes important.
Speaker 3: You ask questions.
Speaker 1: But you also do research where you can disaggregate firms,
Speaker 1: you can disaggregate prices, and you can ask where do
Speaker 1: we see price pressure.
Speaker 3: And how do we think they will evolve.
Speaker 1: That's so that's important, and you can't wait because remember
Speaker 1: Monterrey policy as a twelve to eighteen month lag So
Speaker 1: right now we're modestly restrictive, slightly restrictive depending on who
Speaker 1: you talk to. If you have a neutral rate of
Speaker 1: around three percent interest. Remember that's the one with the
Speaker 1: big range. But if you have a neutral rate of interest,
Speaker 1: think this is around three percent. We have some ways
Speaker 1: to go seventy five basis points roughly before we get
Speaker 1: to that level. But we need to get inflation down
Speaker 1: and we need to make sure that it's on a
Speaker 1: good path. I'm certainly looking at AI and productivity growth
Speaker 1: as one mechanism that continues to help us bring inflation
Speaker 1: leve but we also have restrictive policy and other factors
Speaker 1: that are all bringing in.
Speaker 2: How are you thinking about the labor market now, particularly
Speaker 2: post January jobs, which showed essentially the most hiring in
Speaker 2: more than a year. It was an interesting data point.
Speaker 1: Well, you know, one of the things that I'll offer here,
Speaker 1: and it's something probably most of us stone.
Speaker 3: I mean, you don't look at.
Speaker 1: I'd look at it a lot, but is that a
Speaker 1: lot of the job growth in our nation right now
Speaker 1: is located in health care and education. And while it's
Speaker 1: not bad to have jobs growing in health care and education,
Speaker 1: if you look at the rest of the economy, there
Speaker 1: hasn't really been any job growth, and in fact, there's
Speaker 1: been job decline, you know, negative job growth.
Speaker 3: Basically job losses.
Speaker 1: And so that just makes me put an underscore on
Speaker 1: this idea that the labor market has a no hiring,
Speaker 1: no firing that's already making you a little vulnerable to
Speaker 1: a negative shock pushing you below.
Speaker 3: But also if all your jobs.
Speaker 1: Are in health care and education, think of all those
Speaker 1: workers trained for other sectors who are not and are
Speaker 1: not getting opportunities.
Speaker 3: And I think that's.
Speaker 1: Where, you know, we have more work to do to
Speaker 1: make sure that there's no vulnerability doesn't turn into fragility.
Speaker 1: But that's less about AI and more about the diversified
Speaker 1: growth in the economy. And if companies are able to
Speaker 1: really see positive output growth as uncertainty for the positive
Speaker 1: demand growth as the uncertainty decreases, then I think, you know,
Speaker 1: that's a possibility that would be a positive boost for
Speaker 1: the economy. So then it's about should we look at
Speaker 1: a positive boost for the economy as an inflationary event
Speaker 1: or should we think that a positive boost for the
Speaker 1: economy comes with AI and doesn't actually induce inflation.
Speaker 2: So diversity in the economy is where I want to
Speaker 2: end it before we take audience questions. One of the
Speaker 2: features on the show regularly is compensation in the field
Speaker 2: of AI, stock based compensation, competitive salaries, the newly minted
Speaker 2: millionaires in the field, who you know buying property in
Speaker 2: San Francisco but within the twelfth district. One of the
Speaker 2: things I always reflect on is if I drive from
Speaker 2: the Bay Area down to Socou on the five or
Speaker 2: the one to one, it's the agricultural sector this state
Speaker 2: in particular. But you could expand that to the other
Speaker 2: regions of the district. There's a big sort of contrast there.
Speaker 2: Could you reflect on both, you know, what you see
Speaker 2: at the high end of the tech sector and what
Speaker 2: you do or do not see in agriculture. Feeling benefit
Speaker 2: from AI, well.
Speaker 3: You know, it's interesting.
Speaker 1: So we have, as I said, we have roundtables, and
Speaker 1: I have one this morning, but I have them with
Speaker 1: all kinds of industries. I like to do them by industry.
Speaker 1: So we had an agricultural roundtable. How are you using
Speaker 1: AI surprisingly for ahead of where you'd think, right, they're
Speaker 1: using it to do everything from you know, think about
Speaker 1: idea generation. How do you get better crops, more weather resistant,
Speaker 1: drought resistant, fire resistant, you know, there's all smoke resistant.
Speaker 1: AI can help there because it can help generate ideas.
Speaker 1: Another thing they're doing is using AI to think about
Speaker 1: what's the right planting season?
Speaker 3: Right, how do I forecast weather?
Speaker 1: It's predictive, it's predictive and so, and then of course
Speaker 1: using it in their plants and processing to help augment
Speaker 1: their technology along the production line. So AI is something.
Speaker 1: This is why I think it's more pervasive than many understand,
Speaker 1: is that we've had travel and entertainment, We've had consumer retail,
Speaker 1: we've had builders, commercial developers, agricultural, you name it. Everybody's
Speaker 1: trying to see how this can make their business work better.
Speaker 1: And the question is when we finished this part which
Speaker 1: I think we've been in of using it for cost
Speaker 1: management and just getting your budgets right, is it going
Speaker 1: to start to change into revenue generation etc. We're seeing
Speaker 1: the seeds of that, using it for product development, etc.
Speaker 1: But that's the uncertainty around this is when does it
Speaker 1: move from something that's just in the development stages and
Speaker 1: with electricity, the wealthy urban areas had it and the
Speaker 1: rural areas didn't. In in this could this go faster?
Speaker 1: Is the diffusion of AI and its use cases faster?
Speaker 1: And we had a great discussion at this roundtable this morning,
Speaker 1: and the tail to share and I'm not sharing all
Speaker 1: of our points, so it is still Chathamhouse rules, guys.
Speaker 1: But seriously, the learning is you know, there's a lot
Speaker 1: of perspectives out there that say that AI could be
Speaker 1: an equalizing force, and I think we need to interrogate
Speaker 1: is an an equalizing force? As vigorously as we interrogate,
Speaker 1: could it be driving further inequality. I don't think we
Speaker 1: know the answer to that, and I under I'll end
Speaker 1: with this.
Speaker 3: In the end, the decision is going to be ours.
Speaker 3: It's not gonna be the technologies.
Speaker 1: The technologies don't you know, kind of inherently decide.
Speaker 3: We decide.
Speaker 2: We're going to take a couple of quick questions from
Speaker 2: the audience. But while we find the mic, oh, we
Speaker 2: have some in advance. I know that in the room.
Speaker 2: We're here at San Jose State University, which.
Speaker 3: I'm very excited to be at.
Speaker 2: You know, there are there are those that will soon
Speaker 2: be going into the workforce here. One of them is
Speaker 2: student questioned tough one, what advice do you have for
Speaker 2: new economists, especially those with a desire to enter public service.
Speaker 2: We got in a little bit about how the fed
Speaker 2: and fed a reserve Bank of San Francisco is or
Speaker 2: isn't using AI but reflect on that.
Speaker 1: Sure absolutely, So first of all, I will just say
Speaker 1: thank you. You're an economist and you're going into public service. Fantastic. Seriously,
Speaker 1: we need people like yourselves who are interested in doing this.
Speaker 1: This is a very fantastic career. I would call it
Speaker 1: a vocation to be in public service and serving on
Speaker 1: the types of things that are the federal reserves and
Speaker 1: other public institutions missions.
Speaker 3: So that's important.
Speaker 1: The important thing about public service that I think is
Speaker 1: overlooked is one of the biggest skills you have to
Speaker 1: have as an economist is being a detective. And a
Speaker 1: detective never gets satisfied by looking at one thing. You
Speaker 1: test your theories, you dig deeper. You're never really satisfied.
Speaker 1: You know, people ask me, Mary, why are you constantly
Speaker 1: curious and never really satisfied with the answers? And I said,
Speaker 1: because you basically, the minute you get confident, you lose.
Speaker 1: You want to be confident in the moment and humble
Speaker 1: enough to ask again, is this right?
Speaker 3: And why would it be wrong? And how do you
Speaker 3: do that?
Speaker 1: So that's an important thing I see that you know,
Speaker 1: AI is a technology, it's not a miracle, and so
Speaker 1: it's about how you find a way to relate to
Speaker 1: AI that makes you better, a better detective if you're
Speaker 1: an economist, a better public servant if you choose to
Speaker 1: work in that field.
Speaker 3: And that's how I use it.
Speaker 1: I'm always trying to make myself better at serving those
Speaker 1: who I've got the responsibility to serve, and doing that
Speaker 1: with a technology or with just being out in the
Speaker 1: factory floor and learning how.
Speaker 3: Businesses are doing it. That's the magic there.
Speaker 1: So you don't get yourself monoligned into only one skill.
Speaker 1: It's really about having the detective range of skills and
Speaker 1: recognizing those skills have to change to meet a changing environment,
Speaker 1: but to also meet the moment. The skills I developed
Speaker 1: in the mid nineties, I've certainly had to change and
Speaker 1: augment those to be able to do my job today
Speaker 1: present daily.
Speaker 2: Quite a few of the other questions are on the
Speaker 2: other side of the remit, which is regulation. You know,
Speaker 2: in your speech you mentioned that financial services the financial
Speaker 2: sector early adopters in many ways, and the question is
Speaker 2: how do you balance regulation that ensure safety within the
Speaker 2: financial system but also allows them to innovate, move faster.
Speaker 1: So I do have to say because this is a
Speaker 1: weird aspect of fetter reserve.
Speaker 3: I don't know if it's weird.
Speaker 1: I think it's right, but it's a unique aspect of
Speaker 1: fetter reserves system. The Reserve Bank presidents don't do any
Speaker 1: regular superviser and we don't even do any supervision. That's
Speaker 1: all left with Vice Chairbowman and she at the border governors,
Speaker 1: and the rules get made by the full border governors,
Speaker 1: not the Reserve Bank presidents. That said, we can talk
Speaker 1: about regulation more generally, not just in financial services. And
Speaker 1: there's always attention. If you're an economist, you know this,
Speaker 1: if your business you know this, right, there's always attension.
Speaker 1: If you let fully unregulated innovation occur, you could do
Speaker 1: customer and consumer harm.
Speaker 3: If you do so much regulation.
Speaker 1: That no innovation occurs, well then you will end in stasis.
Speaker 1: And so somewhere in the middle is where the nation
Speaker 1: has to go. Nations, and we have historically had a
Speaker 1: very robust financial sector in the United States that's facilitated
Speaker 1: a lot of intermediation and growth and sort of allowed
Speaker 1: us to be the country that we've been in terms
Speaker 1: of doing.
Speaker 3: Things so we don't want that to stop.
Speaker 1: But as new tools and technologies come out, it's not
Speaker 1: about cutting them off. It's really about thinking about how
Speaker 1: they can be done safely but still innovatively. And I
Speaker 1: think that magic place is not something you get to
Speaker 1: and then you're always there. It's constant recalibration, constantly asking
Speaker 1: the question, you know, the bridle is too tight, or
Speaker 1: the reins too tight, or are they too lose. It's
Speaker 1: very much like monetary policy in that way. You know,
Speaker 1: you don't get to a point and say great, we
Speaker 1: want victory. You actually are always know if you've ever
Speaker 1: ridden a horse, and if you haven't, I apologize, But
Speaker 1: if you've ever ridden a horse, it's not my first vocation.
Speaker 1: You know, if you pull too tight it stops on
Speaker 1: a diamond, you're over the head, And if you let
Speaker 1: go too much, it runs too fast and you're over
Speaker 1: the back. So it's basically trying to manage the bridles
Speaker 1: so that you get the innovation you want without exposing
Speaker 1: consumers or other businesses or the society to harm.
Speaker 2: Let me ask a final question and will end on
Speaker 2: I guess a positive note. Oh good? Which data sets
Speaker 2: and what you see in the real world, because you
Speaker 2: still go out into the real world, gives you most
Speaker 2: optimism about the impact that AI will have on the
Speaker 2: US economy, and specifically that of the twelfth twelfth District.
Speaker 1: So I will say that, you know, when I first
Speaker 1: came to this job in nineteen ninety six, I am
Speaker 1: going to work at the San Francisco feder Reserve. And
Speaker 1: I had been to California a year before, down at
Speaker 1: Southern California at the Rand Institute, and I remember going
Speaker 1: to a conference there and we met a lot of
Speaker 1: business people thinking about not AI, but something else. And
Speaker 1: I came home and I told my wife, We've got
Speaker 1: to go there. It's filled with entrepreneurs. It's filled with
Speaker 1: people who have never heard the word no. They just
Speaker 1: heard why not right. And what's interesting about the twelfth
Speaker 1: District is all of those people don't live in California.
Speaker 3: They live in Utah, they live in Idaho, they live
Speaker 3: in Vegas. You know, they live.
Speaker 1: In the entire inter mountain west west of the Rockies.
Speaker 1: And I'm not saying anything about other places. They're all
Speaker 1: very innovative too, But there is something here that gives
Speaker 1: me optimism because it's not that people say, well, AI
Speaker 1: is coming, and let me figure out how to you know,
Speaker 1: not be eaten up by it. It's like AI is here,
Speaker 1: let's figure out how to harness this tool to create
Speaker 1: a better business, a better economy. And really the thing
Speaker 1: that gets me jazzed and optimistic is people talk about
Speaker 1: a better world. How do we make things better for people?
Speaker 1: How do we change education so there's more quality, How
Speaker 1: do we help the globe have more opportunity? How do
Speaker 1: we harness what's sitting here in front of us with
Speaker 1: all these people, into something powerful that changes lives and livelihoods.
Speaker 3: So that's what gets me optimistic. And it's not this.
Speaker 1: They're just not talking about what they might do. They
Speaker 1: don't stay if I said now, next, later, there's not
Speaker 1: too many conversations with people who live out here who
Speaker 1: talk about way later. They're all talking about now and next.
Speaker 1: That gets me excited.
Speaker 2: With that, all that's left to do is to thank
Speaker 2: the Silicon Valley Leadership Group, San Jose State University, our hosts,
Speaker 2: and some Francisco fore the Reserve Bank President Mary C. Daily,
Speaker 2: thank you very
Speaker 3: Much, Thank you appreciate it.